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Online Action Recognition from Trajectory Occurrence Binary Patterns (ToBPs)

  • Gustavo Garzón
  • Fabio MartínezEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1066)

Abstract

Online action recognition is nowadays a major challenge on computer vision due to uncontrolled scenarios, variability on dynamic action representations, unrestricted capture protocols among many other variations. This work introduces a very compact binary occurrence motion descriptor that allows to recognize actions on partial video-sequences. The proposed approach starts by computing a set of motion trajectories that represent the developed activity. On that regard, a local counting process is performed over bounded regions, and centered at each trajectory, to search for a minimal number of neighboring trajectories. This process is then codified in a vector of binary values (ToBPs) that will create a regional description, at any time of the video sequence, to represent actions. This regional description is obtained by determining the most recurrent binary descriptors in a particular video interval. The final regional descriptor is mapped to a machine learning algorithm to obtain a recognition. The proposed strategy was evaluated on three public datasets, achieving an average accuracy of 70% in tasks of action recognition by using a local descriptor of only 51 values and a regional descriptor of 400 values. This compact description constitute an ideal condition for real-time video applications. The proposed approach achieves a partial recognition above 70% on average accuracy using only the 40% of videos.

Keywords

Action recognition Binary motion patterns Occurrence patterns Motion trajectories 

Notes

Acknowledgements

This work was partially funded by the Universidad Industrial de Santander. The authors acknowledge the Decanato de la Facultad de Ingenierías Fisicomecánicas and the Vicerrectoría de Investigación y Extensión (VIE) of the Universidad Industrial de Santander for supporting this research registered by the project: Reconocimiento continuo de expresiones cortas del lenguaje de señas, with SIVIE code 2430.

References

  1. 1.
    Al-Akam, R., Al-Darraji, S., Paulus, D.: Human action recognition from RGBD videos based on retina model and local binary pattern features. In: 26 Conference on Computer Graphics, Visualization and Computer Vision (WSCG), pp. 1–7 (2018)Google Scholar
  2. 2.
    Al-Ali, S., Milanova, M., Al-Rizzo, H., Fox, V.L.: Human action recognition: contour-based and silhouette-based approaches. In: Computer Vision in Control Systems-2, pp. 11–47. Springer, Heidelberg (2015)Google Scholar
  3. 3.
    Bouwmans, T., Silva, C., Marghes, C., Zitouni, M.S., Bhaskar, H., Frelicot, C.: On the role and the importance of features for background modeling and foreground detection. Comput. Sci. Rev. 28, 26–91 (2018)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)Google Scholar
  5. 5.
    Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. IEEE Trans. Pattern Anal. Mach. Intell. 29(12), 2247–2253 (2007)CrossRefGoogle Scholar
  6. 6.
    Herath, S., Harandi, M., Porikli, F.: Going deeper into action recognition: a survey. Image Vis. Comput. 60, 4–21 (2017)CrossRefGoogle Scholar
  7. 7.
    Laptev, I., Lindeberg, T.: Local descriptors for spatio-temporal recognition. Lect. Notes Comput. Sci. 3667, 91–103 (2006)CrossRefGoogle Scholar
  8. 8.
    Nanni, L., Brahnam, S., Lumini, A.: Local ternary patterns from three orthogonal planes for human action classification. Expert Syst. Appl. 38(5), 5125–5128 (2011)CrossRefGoogle Scholar
  9. 9.
    Nguyen, T.P., Manzanera, A., Vu, N.S., Garrigues, M.: Revisiting LBP-based texture models for human action recognition. In: Iberoamerican Congress on Pattern Recognition, pp. 286–293. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  11. 11.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  12. 12.
    Rahmani, H., Mian, A., Shah, M.: Learning a deep model for human action recognition from novel viewpoints. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 667–681 (2018)CrossRefGoogle Scholar
  13. 13.
    Ryoo, M.S., Aggarwal, J.K.: Spatio-temporal relationship match: video structure comparison for recognition of complex human activities. In: 2009 IEEE 12th international conference on Computer vision, pp. 1593–1600. IEEE (2009)Google Scholar
  14. 14.
    Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition 2004, ICPR 2004, vol. 3, pp. 32–36. IEEE (2004)Google Scholar
  15. 15.
    Wang, H., Kläser, A., Schmid, C., Liu, C.L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103(1), 60–79 (2013)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Yang, Y., Zhang, B., Yang, L., Chen, C., Yang, W.: Action recognition using completed local binary patterns and multiple-class boosting classifier. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 336–340. IEEE (2015)Google Scholar
  17. 17.
    Yeffet, L., Wolf, L.: Local trinary patterns for human action recognition. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 492–497. IEEE (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Biomedical Imaging, Vision and Learning Laboratory (BIVL2ab)Universidad Industrial de SantanderBucaramangaColombia

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